Process Safety, CFD, Machine Learning, Data Analytics

Chi-Yang (Rex) Li

College Station, TX ***** ******@****.*** LinkedIn: chi-yang-rex-li-53b614157

EDUCATION

Texas A&M University, College Station, Texas, United States Doctor of Philosophy in Chemical Engineering GPA:4.0/4.0 Dec 2025 Master of Science in Safety Engineering GPA:4.0/4.0 Dec 2022 Thesis: Machine Learning Based Prediction Models for Flammability Characteristics in the Chemical Industry Graduate Certificate in Industrial Data Analytics GPA: 4.0/4.0 Courses: Engineering Data Analysis (machine learning based), Advanced Quality Control, Design and Analysis of Industrial Experiments, and Simulation Methods and Applications Computational Materials Science Summer School – Fostering Accelerated Scientific Techniques Certificate National Taiwan University, Taipei, Taiwan

Bachelor of Science in Chemical Engineering Jun 2015 SKILLS

Programming Language: Python (Jupyter Notebook, Pandas, NumPy, Scikit-learn, SciPy, SymPy, RDKit, TensorFlow, PyTorch, Matplotlib, Seaborn, PyVista, PyFluent, etc.), R, and C (UDF for Ansys Fluent) Simulation Software: Ansys Fluent Computational Fluid Dynamics (CFD), Ansys Workbench, and DNV Phast Computational techniques: Machine Learning, High Performance Computing, Remote Computing, Linux Operating System, and Slurm Workload Manager

Others: Data analytics, Statistics, technical writing, Microsoft Office, AutoCAD, Process Hazard Analysis, Process Safety Management, Quantitative Risk Analysis, Reliability Engineering, AgenaRisk, and LabVIEW PROFESSIONAL EXPERIENCES

Texas A&M University, College Station, Texas, United States Graduate Research Assistant Jun 2022 – Present

Collaborated with a cross-functional team to integrate machine learning (ML) techniques using Python, quantitative structure-property relationship (QSPR) methods, and databases such as CCDC CSD and NIST- JARVIS for high-throughput computational screening (HTCS) to identify competitive metal-organic frameworks

(MOF) adsorbent candidates for electrochemical CO2 reduction reaction (CO2RR).

Developed Python-based ML tools to predict the potential impact radius of accidental CO2 pipeline releases. This involved leveraging data from CFD simulations performed using Ansys Fluent on Texas A&M High Performance Research Computing (HPRC) system, utilizing PyFluent and Slurm for efficient processing.

Collaborated with a chemical corporation to develop Python-based ML tools for predicting flammability characteristics in a chemical process; integrated predicted data with empirical equations to generate flammability diagrams and assess safe operating limits based on their operating conditions.

Led large-scale LNG fire tests to assess the impact of LNG pool fires on structural beams with fireproof coating, developed LabVIEW program to automate data collection, and conducted data analysis.

Secured funding for two research projects by contributing to successful proposals, demonstrating robust research design, and clearly articulating the significance of each project.

Mentored an undergraduate on advanced carbon capture, utilization, and storage (CCUS) research topics for two semesters, fostering a deep interest in the field. This guidance led to the successful completion of literature review reports and the student's decision to pursue a career in CCUS. Teaching Assistant (Course: Process Safety Engineering) Aug 2024 – Present

Enhanced student success by delivering lectures, holding office hours, and grading assignments, fostering a deep understanding of course material and a positive learning environment. Industrial Safety and Health Association (ISHA) of the R.O.C (Taiwan), Taipei, Taiwan Process Safety Engineer Apr 2017 - Dec 2020

Coordinated an inclusive process safety inspection program for 55 processing plants of Formosa Plastics Group, incorporating performance metrics from API RP 754 and expert feedback to ensure compliance and safety.

Organized systematic inspections with a team for 10 processing plants that experienced catastrophic accidents, identifying the causes and developing key improvements for process safety.

Led a PHA team with applying HAZOP, LOPA, ALOHA simulations, and engineering calculations to conduct a PHA for a project under the detailed design stage at Micron Technology Inc. in Taiwan, providing improvement recommendations to meet company’s criteria on risk management.

Conducted a risk analysis program for LCY Chemical Corp., enhancing process safety practices, improving skills in process hazard analysis, and increasing understanding of process safety.

Developed customized PSM training programs for NANTEX Industry Co. and TASCO Chemical Corp., increasing staff competency in PSM.

Drafted guidelines for PSM, process safety information (PSI), and process safety metrics, and created training materials for process safety incident investigation.

Lectured on over 20 process safety classes covering topics such as PSM, PHA, process safety metrics, process safety incident investigation, and labs for CRW, with attendance ranging from 30 to 100 participants, enhancing knowledge, skills, and abilities in process safety.

Collaborated with the Petrochemical Industry Association of Taiwan (PIAT) and over 20 industry experts to establish a PSM platform for Taiwan's refining and petrochemical sector. PUBLICATIONS

Chen, C., Li, C., Marquez, J. A. D., & Wang, Q. (2024). Lessons from an explosion accident in Linyuan Petrochemical Park of Taiwan: From the perspectives of process safety management. Process Safety Progress. https://doi.org/10.1002/prs.12650

Li, C., Marquez, J. A. D., Hu, P., & Wang, Q. (2023). CO2 pipelines release and dispersion: A review. Journal of Loss Prevention in the Process Industries, 85. https://doi.org/10.1016/j.jlp.2023.105177 Li, C., Zhang, Z., Yang, H., Larranaga, M., Wu, M., McIntosh, J., & Wang, Q. (2024). Examining the Impact of the Presence of the In-House Occupational Safety and Health Professionals on Occupational Safety and Health Performance in Various Industries around the World. (Under review) Yang, H., Li, C., Zou, L., & Wang, Q. (2024). Understanding the effects of natural hazards on chemical emission incidents using machine learning techniques. (Under review) AWARD

2022 ASSP Foundation Academic Scholarship Program

ADDITIONAL INFORMATION

Relocation Willingness: Open to relocating for the right opportunity. Visa Sponsorship: Will require employment visa sponsorship in the future.

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